Intelligent Detection of Electrical Safety Service Work Orders Based on Image Recognition

In response to the issue of discrepancies between the records of electricity safety service work orders and the actual situation, an intelligent detection method based on image recognition technology is introduced to improve the accuracy and efficiency of electricity safety management. Firstly, the article develops an image recognition algorithm based on Convolutional Neural Network (CNN) to analyze electrical equipment and safety environment on site, capture key safety violation indicators such as damage or aging of electrical equipment, equipment grounding status, unauthorized operations, and wiring and cable issues. Then, the article compares the image data with the service ticket records to analyze whether the recorded content is consistent with the actual situation. Finally, through comprehensive analysis of actual data and work order records, non compliant electrical safety issues are identified and detailed compliance assessments and improvement suggestions are provided. After implementation, testing showed that image recognition detection can identify 95% of security risks, improve the consistency between records and actual situations by 40%, and significantly improve the efficiency and accuracy of work order processing. The image recognition based electric safety service work order detection method effectively solves the problem of discrepancies between recorded content and actual situations, improves the automation and intelligence level of electric safety management, and is an effective means to enhance the efficiency of electric power facility safety supervision.